{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f1898f1",
   "metadata": {},
   "source": [
    "## Introduction to the GPT family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "def3ab5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline, set_seed, GPT2Tokenizer, GPT2LMHeadModel, AutoTokenizer\n",
    "from torch import tensor, numel\n",
    "from bertviz import model_view\n",
    "\n",
    "set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe869922",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': \"Hello, I'm a language model and I'm going for a Java one.\\n\\nJava is a language, not a language itself. Java programmers\"},\n",
       " {'generated_text': \"Hello, I'm a language model and I'm a programmer! I teach the English language to kids. Even if a kid doesn't like what I\"},\n",
       " {'generated_text': \"Hello, I'm a language model and I have some nice questions about this stuff [laughter]. I wanted to look into it because I just have this\"}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pipeline object in transformers provides easy access to transformer usage\n",
    "generator = pipeline('text-generation', model='gpt2')\n",
    "\n",
    "# finish the sentence\n",
    "generator(\"Hello, I'm a language model and I\", max_length=30, num_return_sequences=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9687d05c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load up a tokenizer\n",
    "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
    "\n",
    "'Sinan' in tokenizer.get_vocab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68dce33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[46200, 272, 10408, 257, 4950, 1110]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.encode('Sinan loves a beautiful day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "167d0a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sin', 'an', 'Ġloves', 'Ġa', 'Ġbeautiful', 'Ġday']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode a string and then convert the ids back into tokens. Note the Ġ character denoting a space before the token\n",
    "tokenizer.convert_ids_to_tokens(tokenizer.encode('Sinan loves a beautiful day'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4777b3fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[46200, 272, 10408, 257, 4950, 1110]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.encode('Sinan loves a beautiful day')  # ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c892447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[46200,   272, 10408,   257,  4950,  1110]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded = tokenizer.encode('Sinan loves a beautiful day', return_tensors='pt')  # as a pytorch tensor\n",
    "\n",
    "encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3bee3a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a56b028b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load up a tokenizer and model\n",
    "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
    "model = GPT2LMHeadModel.from_pretrained('gpt2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9687f9f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GPT2LMHeadModel(\n",
       "  (transformer): GPT2Model(\n",
       "    (wte): Embedding(50257, 768)\n",
       "    (wpe): Embedding(1024, 768)\n",
       "    (drop): Dropout(p=0.1, inplace=False)\n",
       "    (h): ModuleList(\n",
       "      (0-11): 12 x GPT2Block(\n",
       "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (attn): GPT2Attention(\n",
       "          (c_attn): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): GPT2MLP(\n",
       "          (c_fc): Conv1D()\n",
       "          (c_proj): Conv1D()\n",
       "          (act): NewGELUActivation()\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7278e9ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[46200,   272, 10408,   257,  4950,  1110]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "68795152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.0295, -0.0479,  0.1235,  ..., -0.1539, -0.0276,  0.0994],\n",
       "         [-0.1253, -0.0356,  0.0570,  ..., -0.0876, -0.0628,  0.0048],\n",
       "         [-0.0758, -0.0843, -0.0159,  ..., -0.2232, -0.0390, -0.0899],\n",
       "         [-0.0506,  0.0056,  0.0471,  ...,  0.0425,  0.0735, -0.0384],\n",
       "         [ 0.0535,  0.1676,  0.0133,  ...,  0.0880,  0.0411,  0.1544],\n",
       "         [-0.1379, -0.0294, -0.0026,  ..., -0.0966, -0.0726,  0.1160]]],\n",
       "       grad_fn=<EmbeddingBackward0>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.transformer.wte(encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "541d5c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 6, 768])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.transformer.wte(encoded).shape  # 1 item in batch x 6 tokens x token dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "78d1d442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 6, 768])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.transformer.wpe(tensor([0, 1, 2, 3, 4, 5]).reshape(1, 6)).shape  # manually create position vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b8200f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 6, 768])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create GPT input\n",
    "initial_input = model.transformer.wte(encoded) + model.transformer.wpe(tensor([0, 1, 2, 3, 4, 5]).reshape(1, 6))\n",
    "\n",
    "initial_input.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97a70fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.0107, -0.2453,  0.1275,  ..., -0.1969,  0.0006,  0.1539],\n",
       "         [-0.1013, -0.0894, -0.0378,  ..., -0.0534, -0.0527,  0.0046],\n",
       "         [-0.0716, -0.1690,  0.0386,  ..., -0.2034, -0.0197, -0.1113],\n",
       "         [-0.0509, -0.0682,  0.1526,  ...,  0.0527,  0.0912, -0.0455],\n",
       "         [ 0.0612,  0.1425,  0.1402,  ...,  0.0964,  0.0510,  0.1474],\n",
       "         [-0.1283, -0.0632,  0.1287,  ..., -0.0907, -0.0655,  0.1085]]],\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_input = model.transformer.drop(initial_input)  # run our input through the model's initual dropout later\n",
    "initial_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "55ce36f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=768, out_features=50257, bias=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.lm_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4066d604",
   "metadata": {},
   "outputs": [],
   "source": [
    "for module in model.transformer.h:  # run the initial_input through every decoder in the stack\n",
    "    initial_input = module(initial_input)[0]\n",
    "    \n",
    "initial_input = model.transformer.ln_f(initial_input)  # and then the final layer norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "798ed752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.0542, -0.0179, -0.3388,  ..., -0.0948, -0.1067,  0.0129],\n",
       "         [-0.4805,  0.1008, -0.7313,  ...,  0.0471, -0.4113,  0.0902],\n",
       "         [ 0.0344, -0.2259, -0.5293,  ..., -0.1202,  0.1355,  0.2287],\n",
       "         [-0.2374,  0.1787,  0.1845,  ..., -0.4057, -0.3617, -0.1861],\n",
       "         [ 0.0235,  0.1212, -1.0182,  ..., -0.0597,  0.0020, -0.2220],\n",
       "         [ 0.1206, -0.5034, -1.5260,  ..., -0.3367, -0.2821, -0.0410]]],\n",
       "       grad_fn=<NativeLayerNormBackward0>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11780d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(True)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same as just running through the model\n",
    "(initial_input == model(encoded, output_hidden_states=True).hidden_states[-1]).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7162862a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 6, 50257])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(encoded).logits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d03c5d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of params: 124,439,808\n"
     ]
    }
   ],
   "source": [
    "total_params = 0\n",
    "for param in model.parameters():\n",
    "    total_params += numel(param)\n",
    "    \n",
    "print(f'Number of params: {total_params:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3e7ed76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "274aac4e",
   "metadata": {},
   "source": [
    "## Masked multi-headed attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de1da310",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d20f2219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phrase = 'My friend was right about this class. It is so fun!'\n",
    "encoded_phrase = tokenizer(phrase, return_tensors='pt')\n",
    "\n",
    "response = model(**encoded_phrase, output_attentions=True, output_hidden_states=True)\n",
    "\n",
    "len(response.attentions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "acc9e4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([[3666, 1545,  373,  826,  546,  428, 1398,   13,  632,  318,  523, 1257,\n",
       "            0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3fe66d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 12, 13, 13])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.attentions[-1].shape  # represtnations from the final decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f2337791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 13])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_phrase['input_ids'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "85eec46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['My',\n",
       " 'Ġfriend',\n",
       " 'Ġwas',\n",
       " 'Ġright',\n",
       " 'Ġabout',\n",
       " 'Ġthis',\n",
       " 'Ġclass',\n",
       " '.',\n",
       " 'ĠIt',\n",
       " 'Ġis',\n",
       " 'Ġso',\n",
       " 'Ġfun',\n",
       " '!']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = tokenizer.convert_ids_to_tokens(encoded_phrase['input_ids'][0])\n",
    "\n",
    "tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a12d5b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>My</th>\n",
       "      <th>Ġfriend</th>\n",
       "      <th>Ġwas</th>\n",
       "      <th>Ġright</th>\n",
       "      <th>Ġabout</th>\n",
       "      <th>Ġthis</th>\n",
       "      <th>Ġclass</th>\n",
       "      <th>.</th>\n",
       "      <th>ĠIt</th>\n",
       "      <th>Ġis</th>\n",
       "      <th>Ġso</th>\n",
       "      <th>Ġfun</th>\n",
       "      <th>!</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>My</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġfriend</th>\n",
       "      <td>0.968</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġwas</th>\n",
       "      <td>0.824</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġright</th>\n",
       "      <td>0.979</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġabout</th>\n",
       "      <td>0.979</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġthis</th>\n",
       "      <td>0.924</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġclass</th>\n",
       "      <td>0.946</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>.</th>\n",
       "      <td>0.691</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ĠIt</th>\n",
       "      <td>0.318</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġis</th>\n",
       "      <td>0.331</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġso</th>\n",
       "      <td>0.141</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġfun</th>\n",
       "      <td>0.532</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>!</th>\n",
       "      <td>0.527</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            My  Ġfriend   Ġwas  Ġright  Ġabout  Ġthis  Ġclass      .    ĠIt  \\\n",
       "My       1.000    0.000  0.000   0.000   0.000  0.000   0.000  0.000  0.000   \n",
       "Ġfriend  0.968    0.032  0.000   0.000   0.000  0.000   0.000  0.000  0.000   \n",
       "Ġwas     0.824    0.145  0.031   0.000   0.000  0.000   0.000  0.000  0.000   \n",
       "Ġright   0.979    0.008  0.007   0.005   0.000  0.000   0.000  0.000  0.000   \n",
       "Ġabout   0.979    0.008  0.004   0.005   0.005  0.000   0.000  0.000  0.000   \n",
       "Ġthis    0.924    0.031  0.007   0.006   0.016  0.016   0.000  0.000  0.000   \n",
       "Ġclass   0.946    0.005  0.001   0.001   0.001  0.002   0.044  0.000  0.000   \n",
       ".        0.691    0.013  0.003   0.003   0.002  0.006   0.269  0.013  0.000   \n",
       "ĠIt      0.318    0.003  0.003   0.003   0.006  0.018   0.599  0.018  0.032   \n",
       "Ġis      0.331    0.006  0.002   0.002   0.003  0.018   0.533  0.013  0.062   \n",
       "Ġso      0.141    0.010  0.005   0.004   0.004  0.026   0.645  0.023  0.096   \n",
       "Ġfun     0.532    0.015  0.003   0.002   0.001  0.002   0.317  0.029  0.035   \n",
       "!        0.527    0.008  0.003   0.001   0.001  0.003   0.245  0.026  0.034   \n",
       "\n",
       "           Ġis    Ġso   Ġfun      !  \n",
       "My       0.000  0.000  0.000  0.000  \n",
       "Ġfriend  0.000  0.000  0.000  0.000  \n",
       "Ġwas     0.000  0.000  0.000  0.000  \n",
       "Ġright   0.000  0.000  0.000  0.000  \n",
       "Ġabout   0.000  0.000  0.000  0.000  \n",
       "Ġthis    0.000  0.000  0.000  0.000  \n",
       "Ġclass   0.000  0.000  0.000  0.000  \n",
       ".        0.000  0.000  0.000  0.000  \n",
       "ĠIt      0.000  0.000  0.000  0.000  \n",
       "Ġis      0.030  0.000  0.000  0.000  \n",
       "Ġso      0.041  0.003  0.000  0.000  \n",
       "Ġfun     0.014  0.003  0.047  0.000  \n",
       "!        0.012  0.007  0.046  0.089  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Layer index 9, head 0. Check out the almost 60% attention the token it is giving to the token class\n",
    "arr = response.attentions[9][0][0]\n",
    "\n",
    "n_digits = 3\n",
    "\n",
    "attention_df = pd.DataFrame((torch.round(arr * 10**n_digits) / (10**n_digits)).detach()).applymap(float)\n",
    "\n",
    "attention_df.columns = tokens\n",
    "attention_df.index = tokens\n",
    "\n",
    "attention_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c9cc57a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"></script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "      \n",
       "        <div id=\"bertviz-4ab2530e69dd42f7961244e3643094ad\" style=\"font-family:'Helvetica Neue', Helvetica, Arial, sans-serif;\">\n",
       "            <span style=\"user-select:none\">\n",
       "                \n",
       "            </span>\n",
       "            <div id='vis'></div>\n",
       "        </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "/**\n",
       " * @fileoverview Transformer Visualization D3 javascript code.\n",
       " *\n",
       " * Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n",
       " *\n",
       " * Change log:\n",
       " *\n",
       " * 02/01/19  Jesse Vig   Initial implementation\n",
       " * 12/31/20  Jesse Vig   Support multiple visualizations in single notebook.\n",
       " * 01/19/21  Jesse Vig   Support light/dark modes\n",
       " * 02/06/21  Jesse Vig   Move require config from separate jupyter notebook step\n",
       " * 05/03/21  Jesse Vig   Adjust visualization height dynamically\n",
       " * 03/23/22  Daniel SC   Update requirement URLs for d3 and jQuery (source of bug not allowing end result to be displayed on browsers)\n",
       " **/\n",
       "\n",
       "require.config({\n",
       "  paths: {\n",
       "      d3: 'https://cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min',\n",
       "    jquery: 'https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
       "  }\n",
       "});\n",
       "\n",
       "requirejs(['jquery', 'd3'], function($, d3) {\n",
       "\n",
       "        const params = {\"attention\": [{\"name\": null, \"attn\": [[[[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.9192317128181458, 0.08076826483011246, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.6462416052818298, 0.07439826428890228, 0.2793600261211395, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.5847381949424744, 0.16063930094242096, 0.1644212156534195, 0.09020126610994339, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.5515730977058411, 0.1461305022239685, 0.1782718002796173, 0.08112455904483795, 0.04290005564689636, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.5408418774604797, 0.09085913002490997, 0.09172699600458145, 0.10820916295051575, 0.08696983754634857, 0.08139307051897049, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.602331817150116, 0.18704698979854584, 0.04078644886612892, 0.01291027944535017, 0.039436765015125275, 0.04187371954321861, 0.07561411708593369, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.5482222437858582, 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0.08445993065834045, 0.0, 0.0, 0.0], [0.3201606273651123, 0.009511089883744717, 0.02134890668094158, 0.004720308817923069, 0.013046189211308956, 0.034501805901527405, 0.13969281315803528, 0.008988822810351849, 0.09442975372076035, 0.3206513524055481, 0.03294837474822998, 0.0, 0.0], [0.40046456456184387, 0.025749078020453453, 0.0282191913574934, 0.005882462952286005, 0.01294319424778223, 0.01564154401421547, 0.08395567536354065, 0.2168889343738556, 0.05434432625770569, 0.05227838456630707, 0.04368344694375992, 0.05994926020503044, 0.0], [0.6322535872459412, 0.062426380813121796, 0.025440124794840813, 0.009289502166211605, 0.019578589126467705, 0.010334123857319355, 0.030978549271821976, 0.07678186148405075, 0.019628318026661873, 0.01852595992386341, 0.01749291457235813, 0.023062540218234062, 0.0542074553668499]]]], \"left_text\": [\"My\", \" friend\", \" was\", \" right\", \" about\", \" this\", \" class\", \".\", \" It\", \" is\", \" so\", \" fun\", \"!\"], \"right_text\": [\"My\", \" friend\", \" was\", \" right\", \" about\", \" this\", \" class\", \".\", \" It\", \" is\", \" so\", \" fun\", \"!\"]}], \"default_filter\": \"0\", \"display_mode\": \"dark\", \"root_div_id\": \"bertviz-4ab2530e69dd42f7961244e3643094ad\", \"include_layers\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"include_heads\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"total_heads\": 12} is a template marker that is replaced by actual params.\n",
       "        const config = {};\n",
       "\n",
       "        const MIN_X = 0;\n",
       "        const MIN_Y = 0;\n",
       "        const DIV_WIDTH = 970;\n",
       "        const THUMBNAIL_PADDING = 5;\n",
       "        const DETAIL_WIDTH = 300;\n",
       "        const DETAIL_ATTENTION_WIDTH = 140;\n",
       "        const DETAIL_BOX_WIDTH = 80;\n",
       "        const DETAIL_BOX_HEIGHT = 18;\n",
       "        const DETAIL_PADDING = 15;\n",
       "        const ATTN_PADDING = 0;\n",
       "        const DETAIL_HEADING_HEIGHT = 25;\n",
       "        const HEADING_TEXT_SIZE = 15;\n",
       "        const HEADING_PADDING = 5;\n",
       "        const TEXT_SIZE = 13;\n",
       "        const TEXT_PADDING = 5;\n",
       "        const LAYER_COLORS = d3.schemeCategory10;\n",
       "        const PALETTE = {\n",
       "            'light': {\n",
       "                'text': 'black',\n",
       "                'background': 'white',\n",
       "                'highlight': '#F5F5F5'\n",
       "            },\n",
       "            'dark': {\n",
       "                'text': '#ccc',\n",
       "                'background': 'black',\n",
       "                'highlight': '#222'\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function render() {\n",
       "\n",
       "            // Set global state variables\n",
       "\n",
       "            var attData = config.attention[config.filter];\n",
       "            config.leftText = attData.left_text;\n",
       "            config.rightText = attData.right_text;\n",
       "            config.attn = attData.attn;\n",
       "            config.numLayers = config.attn.length;\n",
       "            config.numHeads = config.attn[0].length;\n",
       "            config.thumbnailBoxHeight = 7 * (12 / config.totalHeads);\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            config.thumbnailHeight = Math.max(config.leftText.length, config.rightText.length) * config.thumbnailBoxHeight + 2 * THUMBNAIL_PADDING;\n",
       "            config.thumbnailWidth = (DIV_WIDTH - axisSize) / config.totalHeads;\n",
       "            config.detailHeight = Math.max(config.leftText.length, config.rightText.length) * DETAIL_BOX_HEIGHT + 2 * DETAIL_PADDING + DETAIL_HEADING_HEIGHT;\n",
       "            config.divHeight = Math.max(config.numLayers * config.thumbnailHeight + axisSize, config.detailHeight);\n",
       "\n",
       "            const vis = $(`#${config.rootDivId} #vis`)\n",
       "            vis.empty();\n",
       "            vis.attr(\"height\", config.divHeight);\n",
       "            config.svg = d3.select(`#${config.rootDivId} #vis`)\n",
       "                .append('svg')\n",
       "                .attr(\"width\", DIV_WIDTH)\n",
       "                .attr(\"height\", config.divHeight)\n",
       "                .attr(\"fill\", getBackgroundColor());\n",
       "\n",
       "            renderAxisLabels();\n",
       "\n",
       "            var i;\n",
       "            var j;\n",
       "            for (i = 0; i < config.numLayers; i++) {\n",
       "                for (j = 0; j < config.numHeads; j++) {\n",
       "                    renderThumbnail(i, j);\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function renderAxisLabels() {\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            const tableWidth = config.thumbnailWidth * config.heads.length;\n",
       "            config.svg.append(\"text\")\n",
       "                .text(\"Heads\")\n",
       "                .attr(\"fill\", \"black\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .attr(\"font-size\", HEADING_TEXT_SIZE + \"px\")\n",
       "                .attr(\"x\", axisSize + tableWidth / 2)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", 0)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "            for (let i = 0; i < config.numHeads; i++) {\n",
       "                config.svg.append(\"text\")\n",
       "                    .text(config.heads[i])\n",
       "                    .attr(\"fill\", \"black\")\n",
       "                    .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                    .attr(\"x\", axisSize + (i + .5) * config.thumbnailWidth)\n",
       "                    .attr(\"text-anchor\", \"middle\")\n",
       "                    .attr(\"y\", HEADING_TEXT_SIZE + HEADING_PADDING)\n",
       "                    .attr(\"dy\", TEXT_SIZE);\n",
       "            }\n",
       "            let x = 0;\n",
       "            let y = axisSize + config.thumbnailHeight * config.layers.length / 2;\n",
       "            console.log(\"x\", x, y)\n",
       "            config.svg.append(\"text\")\n",
       "                .text(\"Layers\")\n",
       "                .attr(\"fill\", \"black\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .attr(\"transform\", \"rotate(270, \" + x  + \", \" + y + \")\")\n",
       "                .attr(\"font-size\", HEADING_TEXT_SIZE + \"px\")\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "            for (let i = 0; i < config.numLayers; i++) {\n",
       "                x = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE; // HACK\n",
       "                y = axisSize + (i + .5) * config.thumbnailHeight;\n",
       "                config.svg.append(\"text\")\n",
       "                    .text(config.layers[i])\n",
       "                    .attr(\"fill\", \"black\")\n",
       "                    .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                    .attr(\"x\", x)\n",
       "                    .attr(\"text-anchor\", \"end\")\n",
       "                    .attr(\"y\", y)\n",
       "                    .attr(\"dy\", TEXT_SIZE / 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "\n",
       "        function renderThumbnail(layerIndex, headIndex) {\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING\n",
       "            const x = headIndex * config.thumbnailWidth + axisSize;\n",
       "            const y = layerIndex * config.thumbnailHeight + axisSize;\n",
       "            renderThumbnailAttn(x, y, config.attn[layerIndex][headIndex], layerIndex, headIndex);\n",
       "        }\n",
       "\n",
       "        function renderDetail(att, layerIndex, headIndex) {\n",
       "            const axisSize = TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            var xOffset = .8 * config.thumbnailWidth;\n",
       "            var maxX = DIV_WIDTH;\n",
       "            var maxY = config.divHeight - 3;\n",
       "            var leftPos = axisSize + headIndex * config.thumbnailWidth;\n",
       "            var x = leftPos + THUMBNAIL_PADDING + xOffset;\n",
       "            if (x < MIN_X) {\n",
       "                x = MIN_X;\n",
       "            } else if (x + DETAIL_WIDTH > maxX) {\n",
       "                x = leftPos + THUMBNAIL_PADDING - DETAIL_WIDTH + 8;\n",
       "            }\n",
       "            var posLeftText = x;\n",
       "            var posAttention = posLeftText + DETAIL_BOX_WIDTH;\n",
       "            var posRightText = posAttention + DETAIL_ATTENTION_WIDTH;\n",
       "            var thumbnailHeight = Math.max(config.leftText.length, config.rightText.length) * config.thumbnailBoxHeight + 2 * THUMBNAIL_PADDING;\n",
       "            var yOffset = 20;\n",
       "            var y = layerIndex * thumbnailHeight + THUMBNAIL_PADDING + yOffset;\n",
       "            if (y < MIN_Y) {\n",
       "                y = MIN_Y;\n",
       "            } else if (y + config.detailHeight > maxY) {\n",
       "                y = maxY - config.detailHeight;\n",
       "            }\n",
       "            renderDetailFrame(x, y, layerIndex);\n",
       "            y = y + DETAIL_PADDING;\n",
       "            renderDetailHeading(x, y, layerIndex, headIndex);\n",
       "            y = y + DETAIL_HEADING_HEIGHT;\n",
       "            renderDetailText(config.leftText, \"leftText\", posLeftText, y , layerIndex);\n",
       "            renderDetailAttn(posAttention, y, att, layerIndex, headIndex);\n",
       "            renderDetailText(config.rightText, \"rightText\", posRightText, y, layerIndex);\n",
       "        }\n",
       "\n",
       "        function renderDetailHeading(x, y, layerIndex, headIndex) {\n",
       "            var fillColor = getTextColor();\n",
       "            config.svg.append(\"text\")\n",
       "                .classed(\"detail\", true)\n",
       "                .text('Layer ' + config.layers[layerIndex] + \", Head \" + config.heads[headIndex])\n",
       "                .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .style(\"cursor\", \"default\")\n",
       "                .style(\"-webkit-user-select\", \"none\")\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .attr(\"x\", x + DETAIL_WIDTH / 2)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", DETAIL_HEADING_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_WIDTH)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "        }\n",
       "\n",
       "        function renderDetailText(text, id, x, y, layerIndex) {\n",
       "            var tokenContainer = config.svg.append(\"svg:g\")\n",
       "                .classed(\"detail\", true)\n",
       "                .selectAll(\"g\")\n",
       "                .data(text)\n",
       "                .enter()\n",
       "                .append(\"g\");\n",
       "\n",
       "            var fillColor = getTextColor();\n",
       "\n",
       "            tokenContainer.append(\"rect\")\n",
       "                .classed(\"highlight\", true)\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .style(\"opacity\", 0.0)\n",
       "                .attr(\"height\", DETAIL_BOX_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_BOX_WIDTH)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", function (d, i) {\n",
       "                    return y + i * DETAIL_BOX_HEIGHT;\n",
       "                });\n",
       "\n",
       "            var textContainer = tokenContainer.append(\"text\")\n",
       "                .classed(\"token\", true)\n",
       "                .text(function (d) {\n",
       "                    return d;\n",
       "                })\n",
       "                .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                .style(\"cursor\", \"default\")\n",
       "                .style(\"-webkit-user-select\", \"none\")\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", function (d, i) {\n",
       "                    return i * DETAIL_BOX_HEIGHT + y;\n",
       "                })\n",
       "                .attr(\"height\", DETAIL_BOX_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_BOX_WIDTH)\n",
       "                .attr(\"dy\", TEXT_SIZE);\n",
       "\n",
       "            if (id == \"leftText\") {\n",
       "                textContainer.style(\"text-anchor\", \"end\")\n",
       "                    .attr(\"dx\", DETAIL_BOX_WIDTH - 2);\n",
       "                tokenContainer.on(\"mouseover\", function (d, index) {\n",
       "                    highlightSelection(index);\n",
       "                });\n",
       "                tokenContainer.on(\"mouseleave\", function () {\n",
       "                    unhighlightSelection();\n",
       "                });\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function highlightSelection(index) {\n",
       "            config.svg.select(\"#leftText\")\n",
       "                .selectAll(\".highlight\")\n",
       "                .style(\"opacity\", function (d, i) {\n",
       "                    return i == index ? 1.0 : 0.0;\n",
       "                });\n",
       "            config.svg.selectAll(\".attn-line-group\")\n",
       "                .style(\"opacity\", function (d, i) {\n",
       "                    return i == index ? 1.0 : 0.0;\n",
       "                });\n",
       "        }\n",
       "\n",
       "        function unhighlightSelection() {\n",
       "            config.svg.select(\"#leftText\")\n",
       "                .selectAll(\".highlight\")\n",
       "                .style(\"opacity\", 0.0);\n",
       "            config.svg.selectAll(\".attn-line-group\")\n",
       "                .style(\"opacity\", 1);\n",
       "        }\n",
       "\n",
       "        function renderThumbnailAttn(x, y, att, layerIndex, headIndex) {\n",
       "\n",
       "            var attnContainer = config.svg.append(\"svg:g\");\n",
       "\n",
       "            var attnBackground = attnContainer.append(\"rect\")\n",
       "                .attr(\"id\", 'attn_background_' + layerIndex + \"_\" + headIndex)\n",
       "                .classed(\"attn_background\", true)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.thumbnailHeight)\n",
       "                .attr(\"width\", config.thumbnailWidth)\n",
       "                .attr(\"stroke-width\", 2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", 0)\n",
       "                .attr(\"fill\", getBackgroundColor());\n",
       "            var x1 = x + THUMBNAIL_PADDING;\n",
       "            var x2 = x1 + config.thumbnailWidth - 14;\n",
       "            var y1 = y + THUMBNAIL_PADDING;\n",
       "\n",
       "            attnContainer.selectAll(\"g\")\n",
       "                .data(att)\n",
       "                .enter()\n",
       "                .append(\"g\") // Add group for each source token\n",
       "                .attr(\"source-index\", function (d, i) { // Save index of source token\n",
       "                    return i;\n",
       "                })\n",
       "                .selectAll(\"line\")\n",
       "                .data(function (d) { // Loop over all target tokens\n",
       "                    return d;\n",
       "                })\n",
       "                .enter() // When entering\n",
       "                .append(\"line\")\n",
       "                .attr(\"x1\", x1)\n",
       "                .attr(\"y1\", function (d) {\n",
       "                    var sourceIndex = +this.parentNode.getAttribute(\"source-index\");\n",
       "                    return y1 + (sourceIndex + .5) * config.thumbnailBoxHeight;\n",
       "                })\n",
       "                .attr(\"x2\", x2)\n",
       "                .attr(\"y2\", function (d, targetIndex) {\n",
       "                    return y1 + (targetIndex + .5) * config.thumbnailBoxHeight;\n",
       "                })\n",
       "                .attr(\"stroke-width\", 2.2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", function (d) {\n",
       "                    return d;\n",
       "                });\n",
       "\n",
       "            var clickRegion = attnContainer.append(\"rect\")\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.thumbnailHeight)\n",
       "                .attr(\"width\", config.thumbnailWidth)\n",
       "                .style(\"opacity\", 0);\n",
       "\n",
       "            clickRegion.on(\"click\", function (d, index) {\n",
       "                var attnBackgroundOther = config.svg.selectAll(\".attn_background\");\n",
       "                attnBackgroundOther.attr(\"fill\", getBackgroundColor());\n",
       "                attnBackgroundOther.attr(\"stroke-opacity\", 0);\n",
       "\n",
       "                config.svg.selectAll(\".detail\").remove();\n",
       "                if (config.detail_layer != layerIndex || config.detail_head != headIndex) {\n",
       "                    renderDetail(att, layerIndex, headIndex);\n",
       "                    config.detail_layer = layerIndex;\n",
       "                    config.detail_head = headIndex;\n",
       "                    attnBackground.attr(\"fill\", getHighlightColor());\n",
       "                    attnBackground.attr(\"stroke-opacity\", .8);\n",
       "                } else {\n",
       "                    config.detail_layer = null;\n",
       "                    config.detail_head = null;\n",
       "                    attnBackground.attr(\"fill\", getBackgroundColor());\n",
       "                    attnBackground.attr(\"stroke-opacity\", 0);\n",
       "                }\n",
       "            });\n",
       "\n",
       "            clickRegion.on(\"mouseover\", function (d) {\n",
       "                d3.select(this).style(\"cursor\", \"pointer\");\n",
       "            });\n",
       "        }\n",
       "\n",
       "        function renderDetailFrame(x, y, layerIndex) {\n",
       "            var detailFrame = config.svg.append(\"rect\")\n",
       "                .classed(\"detail\", true)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.detailHeight)\n",
       "                .attr(\"width\", DETAIL_WIDTH)\n",
       "                .style(\"opacity\", 1)\n",
       "                .attr(\"stroke-width\", 1.5)\n",
       "                .attr(\"stroke-opacity\", 0.7)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex));\n",
       "        }\n",
       "\n",
       "        function renderDetailAttn(x, y, att, layerIndex) {\n",
       "            var attnContainer = config.svg.append(\"svg:g\")\n",
       "                .classed(\"detail\", true)\n",
       "                .attr(\"pointer-events\", \"none\");\n",
       "            attnContainer.selectAll(\"g\")\n",
       "                .data(att)\n",
       "                .enter()\n",
       "                .append(\"g\") // Add group for each source token\n",
       "                .classed('attn-line-group', true)\n",
       "                .attr(\"source-index\", function (d, i) { // Save index of source token\n",
       "                    return i;\n",
       "                })\n",
       "                .selectAll(\"line\")\n",
       "                .data(function (d) { // Loop over all target tokens\n",
       "                    return d;\n",
       "                })\n",
       "                .enter()\n",
       "                .append(\"line\")\n",
       "                .attr(\"x1\", x + ATTN_PADDING)\n",
       "                .attr(\"y1\", function (d) {\n",
       "                    var sourceIndex = +this.parentNode.getAttribute(\"source-index\");\n",
       "                    return y + (sourceIndex + .5) * DETAIL_BOX_HEIGHT;\n",
       "                })\n",
       "                .attr(\"x2\", x + DETAIL_ATTENTION_WIDTH - ATTN_PADDING)\n",
       "                .attr(\"y2\", function (d, targetIndex) {\n",
       "                    return y + (targetIndex + .5) * DETAIL_BOX_HEIGHT;\n",
       "                })\n",
       "                .attr(\"stroke-width\", 2.2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", function (d) {\n",
       "                    return d;\n",
       "                });\n",
       "        }\n",
       "\n",
       "        function getLayerColor(layer) {\n",
       "          return LAYER_COLORS[config.layers[layer] % 10];\n",
       "        }\n",
       "\n",
       "        function getTextColor() {\n",
       "            return PALETTE[config.mode]['text']\n",
       "        }\n",
       "\n",
       "        function getBackgroundColor() {\n",
       "           return PALETTE[config.mode]['background']\n",
       "        }\n",
       "\n",
       "        function getHighlightColor() {\n",
       "           return PALETTE[config.mode]['highlight']\n",
       "        }\n",
       "\n",
       "        function initialize() {\n",
       "            config.attention = params['attention'];\n",
       "            config.filter = params['default_filter'];\n",
       "            config.mode = params['display_mode'];\n",
       "            config.layers = params['include_layers']\n",
       "            config.heads = params['include_heads']\n",
       "            config.totalHeads = params['total_heads']\n",
       "            config.rootDivId = params['root_div_id'];\n",
       "            $(`#${config.rootDivId} #filter`).on('change', function (e) {\n",
       "                config.filter = e.currentTarget.value;\n",
       "                render();\n",
       "            });\n",
       "        }\n",
       "\n",
       "        initialize();\n",
       "        render();\n",
       "\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokens = tokenizer.convert_ids_to_tokens(encoded_phrase['input_ids'][0]) \n",
    "model_view(response.attentions, tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b82dc780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 13, 768])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.hidden_states[-1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1b5760c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ -33.0735,  -32.3349,  -35.2379,  ...,  -38.3576,  -38.4758,\n",
       "           -33.0943],\n",
       "         [ -98.8075,  -98.9729, -105.2267,  ..., -109.0421, -104.7381,\n",
       "          -101.0802],\n",
       "         [-131.2764, -130.8178, -135.2054,  ..., -138.3585, -134.9846,\n",
       "          -134.1658],\n",
       "         ...,\n",
       "         [-120.6479, -122.7568, -127.1870,  ..., -130.4343, -129.5815,\n",
       "          -125.1274],\n",
       "         [ -73.8157,  -78.6168,  -85.3995,  ...,  -91.8287,  -91.7846,\n",
       "           -81.4570],\n",
       "         [-134.7545, -134.9494, -136.2912,  ..., -147.1822, -148.1732,\n",
       "          -130.7045]]], grad_fn=<UnsafeViewBackward0>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1f80594",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3e06305a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 13, 50257])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.logits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "170c473e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sequence up until</th>\n",
       "      <th>Next token with highest probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>My</td>\n",
       "      <td>Ċ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ġfriend</td>\n",
       "      <td>,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ġwas</td>\n",
       "      <td>Ġa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ġright</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ġabout</td>\n",
       "      <td>Ġthat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ġthis</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ġclass</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>.</td>\n",
       "      <td>ĠI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ĠIt</td>\n",
       "      <td>'s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ġis</td>\n",
       "      <td>Ġa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ġso</td>\n",
       "      <td>Ġimportant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ġfun</td>\n",
       "      <td>Ġto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>!</td>\n",
       "      <td>ĠI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sequence up until Next token with highest probability\n",
       "0                 My                                   Ċ\n",
       "1            Ġfriend                                   ,\n",
       "2               Ġwas                                  Ġa\n",
       "3             Ġright                                   .\n",
       "4             Ġabout                               Ġthat\n",
       "5              Ġthis                                   .\n",
       "6             Ġclass                                   .\n",
       "7                  .                                  ĠI\n",
       "8                ĠIt                                  's\n",
       "9                Ġis                                  Ġa\n",
       "10               Ġso                          Ġimportant\n",
       "11              Ġfun                                 Ġto\n",
       "12                 !                                  ĠI"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the top next token in the auto-regressive language modelling task\n",
    "pd.DataFrame(\n",
    "    zip(tokens, tokenizer.convert_ids_to_tokens(response.logits.argmax(2)[0])), \n",
    "    columns=['Sequence up until', 'Next token with highest probability']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8cd539ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "/opt/homebrew/lib/python3.11/site-packages/transformers/generation/utils.py:1268: UserWarning: Input length of input_ids is 4, but `max_length` is set to 4. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'My friend was right about'},\n",
       " {'generated_text': 'My friend was right next'},\n",
       " {'generated_text': 'My friend was right to'},\n",
       " {'generated_text': 'My friend was right and'},\n",
       " {'generated_text': 'My friend was right all'}]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator('My friend was right', max_length=4, num_return_sequences=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8e6e8b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'My friend was right about this class. It is so fun! I love it! I love the'}]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(phrase, max_length=20, num_return_sequences=1, do_sample=False)  # greedy search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2fa15fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': \"My friend was right about this class. It is so fun! I don't expect them to teach\"}]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(phrase, max_length=20, num_return_sequences=1, do_sample=True)  # greedy search with sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff934358",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1643fc51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f4616e0f",
   "metadata": {},
   "source": [
    "## Pre-training GPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9eee5b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline, set_seed\n",
    "from torch import tensor\n",
    "\n",
    "generator = pipeline('text-generation', model='gpt2', tokenizer=tokenizer)\n",
    "set_seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7768633e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9982bb3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'The holocaust was an extremely horrific act, but'},\n",
       " {'generated_text': 'The holocaust was committed by the wicked in all'},\n",
       " {'generated_text': 'The holocaust was inevitable; this is what they'},\n",
       " {'generated_text': 'The holocaust was as little a mystery as possible'},\n",
       " {'generated_text': 'The holocaust was perpetrated by Nazis, the Jews'},\n",
       " {'generated_text': 'The holocaust was the first mass killing in the'},\n",
       " {'generated_text': 'The holocaust was an event of unparalleled magnitude and'},\n",
       " {'generated_text': 'The holocaust was \"the greatest of all time'},\n",
       " {'generated_text': 'The holocaust was an act of violence in which'},\n",
       " {'generated_text': 'The holocaust was just an empty lie,\" he'}]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bias\n",
    "generator(\"The holocaust was\", max_length=10, num_return_sequences=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "629c5b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'Jewish people are still under attack.\"'},\n",
       " {'generated_text': 'Jewish people are also involved in sexual assaults in the'},\n",
       " {'generated_text': 'Jewish people are also among the hardest-working,'},\n",
       " {'generated_text': 'Jewish people are doing their best to help them,'},\n",
       " {'generated_text': 'Jewish people are not treated with equality, they are'},\n",
       " {'generated_text': 'Jewish people are called the \"fearless,'},\n",
       " {'generated_text': 'Jewish people are the most vulnerable. If they are'},\n",
       " {'generated_text': 'Jewish people are often regarded as \"anti-European'},\n",
       " {'generated_text': 'Jewish people are not even the only victims. More'},\n",
       " {'generated_text': 'Jewish people are \"the greatest human beings I know'}]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(\"Jewish people are\", max_length=10, num_return_sequences=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5c19883b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'Christian people are not allowed to carry a handgun in'},\n",
       " {'generated_text': 'Christian people are here and are going to be angry'},\n",
       " {'generated_text': 'Christian people are the ones that should be persecuted under'},\n",
       " {'generated_text': 'Christian people are already well versed in the fact'},\n",
       " {'generated_text': 'Christian people are the natural followers of Allah, whose'},\n",
       " {'generated_text': 'Christian people are often called racists and are often accused'},\n",
       " {'generated_text': 'Christian people are very good. He said about people'},\n",
       " {'generated_text': 'Christian people are very good, but we must be'},\n",
       " {'generated_text': 'Christian people are the most diverse people there are.'},\n",
       " {'generated_text': 'Christian people are really angry about this. What can'}]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(\"Christian people are\", max_length=10, num_return_sequences=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fe80d246",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'The earth is covered in so much snow the sea'},\n",
       " {'generated_text': 'The earth is flat because of this, a total'},\n",
       " {'generated_text': 'The earth is a giant, unshovel'},\n",
       " {'generated_text': 'The earth is an incredibly fertile and arid place'},\n",
       " {'generated_text': 'The earth is a wonderful place to visit,\" W'},\n",
       " {'generated_text': 'The earth is always going to be hotter and cooler'},\n",
       " {'generated_text': 'The earth is slowly melting beneath us. What can'},\n",
       " {'generated_text': 'The earth is flat and nothing will be seen till'},\n",
       " {'generated_text': 'The earth is an ever evolving environment. There are'},\n",
       " {'generated_text': 'The earth is not round, it does not rise'}]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(\"The earth is\", max_length=10, num_return_sequences=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b93acda7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1ec88e66",
   "metadata": {},
   "source": [
    "## Few-shot learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "60177cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Analysis\n",
      "Text: I hate it when my phone battery dies.\n",
      "Sentiment: Negative\n",
      "###\n",
      "Text: My day has been really great!\n",
      "Sentiment: Positive\n",
      "###\n",
      "Text: Not a fan when it is cloudy\n",
      "Sentiment: Negative\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(generator(\"\"\"Sentiment Analysis\n",
    "Text: I hate it when my phone battery dies.\n",
    "Sentiment: Negative\n",
    "###\n",
    "Text: My day has been really great!\n",
    "Sentiment: Positive\n",
    "###\n",
    "Text: Not a fan when it is cloudy\n",
    "Sentiment:\"\"\", top_k=2, temperature=0.1, max_length=55)[0]['generated_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d239b93a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question/Answering\n",
      "C: Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14 percent of its shares and control 56 percent of the stockholder voting power through supervoting stock.\n",
      "Q: When was Google founded?\n",
      "A: 1998\n",
      "###\n",
      "C: Hugging Face is a company which develops social AI-run chatbot applications. It was established in 2016 by Clement Delangue and Julien Chaumond. The company is based in Brooklyn, New York, United States.\n",
      "Q: What does Hugging Face develop?\n",
      "A: social AI-run chatbot applications\n",
      "###\n",
      "C: The New York Jets are a professional American football team based in the New York metropolitan area. The Jets compete in the National Football League (NFL) as a member club of the league's American Football Conference (AFC) East division.\n",
      "Q: What division do the Jets play in?\n",
      "A: American Football Conference (AFC) East\n"
     ]
    }
   ],
   "source": [
    "print(generator(\"\"\"Question/Answering\n",
    "C: Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14 percent of its shares and control 56 percent of the stockholder voting power through supervoting stock.\n",
    "Q: When was Google founded?\n",
    "A: 1998\n",
    "###\n",
    "C: Hugging Face is a company which develops social AI-run chatbot applications. It was established in 2016 by Clement Delangue and Julien Chaumond. The company is based in Brooklyn, New York, United States.\n",
    "Q: What does Hugging Face develop?\n",
    "A: social AI-run chatbot applications\n",
    "###\n",
    "C: The New York Jets are a professional American football team based in the New York metropolitan area. The Jets compete in the National Football League (NFL) as a member club of the league's American Football Conference (AFC) East division.\n",
    "Q: What division do the Jets play in?\n",
    "A:\"\"\", top_k=5, num_beams=2, max_length=215, temperature=0.5)[0]['generated_text'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "982e0492",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Zero Shot Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "11c405c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question/Answering\n",
      "C: The New York Jets are a professional American football team based in the New York metropolitan area. The Jets compete in the National Football League (NFL) as a member club of the league's American Football Conference (AFC) East division.\n",
      "Q: What division do the Jets play in?\n",
      "A: The Jets play in the AFC East.\n",
      "Q:\n"
     ]
    }
   ],
   "source": [
    "# Same question as before, with no previous examples ie Zero-shot learning. Hit or miss\n",
    "print(generator(\n",
    "    '''Question/Answering\n",
    "C: The New York Jets are a professional American football team based in the New York metropolitan area. The Jets compete in the National Football League (NFL) as a member club of the league's American Football Conference (AFC) East division.\n",
    "Q: What division do the Jets play in?\n",
    "A:''',\n",
    "    top_k=5, num_beams=2, max_length=80, temperature=0.5)[0]['generated_text']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4338c6e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Analysis\n",
      "Text: This new music video was so good\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n"
     ]
    }
   ],
   "source": [
    "# Zero-shot doesn't work as much with the sentiment analysis example\n",
    "print(generator(\"\"\"Sentiment Analysis\n",
    "Text: This new music video was so good\n",
    "Sentiment:\"\"\", top_k=2, temperature=0.1, max_length=55)[0]['generated_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2749594b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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